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dc.contributor.authorYüksel, Ulaş
dc.contributor.authorSözer, Hasan
dc.date.accessioned2016-02-15T07:33:19Z
dc.date.available2016-02-15T07:33:19Z
dc.date.issued2013
dc.identifier.issn1063-6773
dc.identifier.urihttp://hdl.handle.net/10679/2186
dc.identifier.urihttp://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6676950&tag=1
dc.descriptionDue to copyright restrictions, the access to the full text of this article is only available via subscription.
dc.description.abstractStatic code analysis tools automatically generate alerts for potential software faults that can lead to failures. However, developers are usually exposed to a large number of alerts. Moreover, some of these alerts are subject to false positives and there is a lack of resources to inspect all the alerts manually. To address this problem, numerous approaches have been proposed for automatically ranking or classifying the alerts based on their likelihood of reporting a critical fault. One of the promising approaches is the application of machine learning techniques to classify alerts based on a set of artifact characteristics. In this work, we evaluate this approach in the context of an industrial case study to classify the alerts generated for a digital TV software. First, we created a benchmark based on this code base by manually analyzing thousands of alerts. Then, we evaluated 34 machine learning algorithms using 10 different artifact characteristics and identified characteristics that have a significant impact. We obtained promising results with respect to the precision of classification.
dc.language.isoengen_US
dc.publisherIEEE
dc.relation.ispartofSoftware Maintenance (ICSM), 2013 29th IEEE International Conference on
dc.rightsrestrictedAccess
dc.titleAutomated classification of static code analysis alerts: a case studyen_US
dc.typeConference paperen_US
dc.peerreviewedyes
dc.publicationstatuspublished
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID(ORCID 0000-0002-2968-4763 & YÖK ID 23178) Sözer, Hasan
dc.contributor.ozuauthorSözer, Hasan
dc.identifier.startpage532
dc.identifier.endpage535
dc.identifier.wosWOS:000332836100080
dc.identifier.doi10.1109/ICSM.2013.89
dc.subject.keywordsAlert classification
dc.subject.keywordsIndustrial case study
dc.subject.keywordsStatic code analysis
dc.identifier.scopusSCOPUS:2-s2.0-84891711063
dc.contributor.ozugradstudentYüksel, Ulaş
dc.contributor.authorMale2


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